How Much Is That Customer Worth? Predicting Tomorrow’s Revenue Today

Customer acquisition and customer retention have historically lived in separate silos within marketing organizations. Those days are ending: CEOs and CMOs are beginning to recognize the true costs of assigning a proxy to actual customer value during the acquisition phase.

Such marketers have found that, with silos in place, they are less competitive in bidding for inventory supply sources. They’re also realizing there are hidden costs to acquiring customers who don’t monetize over time. The cost to acquire and support a customer who falls outside your target customer profile can be much more expensive than properly modeling your target market in the first place.

One of the highest costs is supporting customers who never reach your expectations and then leave dissatisfied with their experience. It’s likely that an unhappy customer may never have been a good target, in which case negative word of mouth could have been avoided with better profiling in the first place.

Advancements in machine learning, the accelerating speed of real-time bidding and the adoption of auction-buying environments all are driving marketers to switch from focusing on acquisition to thinking about true ROI. Deep data integrations, including CRM as well as inventory-management systems, allow marketers to move away from arcane buying processes involving insertion orders and negotiation of placements to newer and more accurate models of Predictive Lifetime Value.

Furthermore, in the next five years, elements that have not traditionally fallen under marketers’ purview will become the CMOs’ responsibility. Just like messaging and audience selection, which have been staple considerations for marketers over past decades, considerations such as inventory management, pricing, and yield, as well as manufacturing timelines, will become primary drivers of media buying.

Marketers that replace traditional strategies with predictive lifetime value models will become more competitive in auction environments, as they will have a better sense of the real value of the ad placements on which they bidding.

Fixing A Broken Process

Here’s how a marketer typically delivers a performance campaign today:

Determine a budget.

Select a target audience.

Select products to highlight in marketing messages.

Choose media channels and publishers to house the ads.

Define price (or negotiate rates depending on the type of media).

Define an attribution structure.

These steps can be simplified by considering the true questions a marketer wants answered: How much profit (or margin) do you want to realize and what is the time horizon in which you expect to earn this money?

Buying models should automatically consider:

Target market(s).

Seasonality.

Hour of day and day of week.

Publisher inventory levels (by audience attribute).

Internal product inventory levels.

Historical product yield.

Sequential product buying trends (Which products lead to further product acquisition? For example, when a customer buys a shirt, are they likely to buy pants next, then a belt, then shoes and then socks?).

These elements will allow advertisers to understand the margin that they can attain over a period of time. Such models need to simply place the products, define pricing and match the elements to an audience that will yield the desired ROI over the predefined time horizon. A good buying model will remove the guesswork from lower-level marketers and allow executives to set accurate expectations with their board members and shareholders.

In the future, marketers will need the following functions from tracking:

Cohort analysis to understand the value of a customer over time.

Maturity curves to determine how long customers continue to add value and when they have reached their full potential. These will also help marketers sequence creative and products, in cases where customers naturally progress from buying another product after having bought a related one.

Value-based bidding: Leveraging data from cohort analysis to update bid values based on increasing (or decreasing) customer worth as they mature as customers.

Affinity models to measure and retarget the common characteristics of good customers and suppress those of bad customers.

Inventory-management system to update pricing based on seasonal demand cycles and scarcity of products.

Automated creative optimization and placement bidding to consider all of these factors.

Predictive Lifetime Value Should Define Acquisition Pricing Tolerance

The last consideration for marketers around lifetime value is that every customer is worth a specific amount. Using a proxy metric, such as CPA, assigns a cost-averaged price to all customers regardless of their lifetime value or potential to mature. This inevitably leads to overpaying for some customers and undervaluing others. Worse yet, in a biddable environment this practice prevents marketers from winning the highest-quality customers because their value gets lumped in with the lowest-quality customers.

Most marketplaces are full of advertisers bidding with proxy metrics, which causes tremendous competition for specific audience and inflates the price for lower-quality audiences. The practice of overbidding for a lower-quality user, combined with a demand surplus for these users, is a recipe for a direct-response marketing failure.

With a predictive lifetime value model, marketers can better anticipate the market demands against their supply and compete more successfully in auction environments. The savviest advertisers have already found ways to take advantage of the current environment and to exploit those who have not yet developed the automation and internal business practices to buy based on true ROI.